Method of correlating brain activity

ABSTRACT

The present invention includes systems and methods of using real-time neurofeedback to improve the correspondence between first-person experience and specific brain activation patterns in a manner that minimally affects the experience itself. The present invention provides meditators the ability to enhance their control over their own brain activity, such as posterior cingulate cortex (PCC) activation. The present invention also provides methods for treating a disease or disorder of a subject by measuring the subject&#39;s brain activity via fMRI, presenting a representation of the measured brain activity to the subject, and instructing the subject to reduce the represented brain activity by altering their meditative state. The present invention also provides a system and method of using fMRI neurofeedback to directly correlate subjective experience with neural activation.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of, and claims priority to,U.S. patent application Ser. No. 13/680,744, filed Nov. 19, 2012, nowallowed, which claims priority under 35 U.S.C. §119(e) to U.S.Provisional Patent Application No. 61/561,871, filed on Nov. 19, 2011,all of which applications are incorporated herein by reference in theirentireties.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under DA000167 andDA029163 awarded by National Institute of Health. The government hascertain rights in the invention.

BACKGROUND OF THE INVENTION

Mind-wandering is not only a common activity, present in roughly 50% ofour awake life, but is also associated with lower levels of happiness(Killingsworth & Gilbert, 2010, Science 330(6006):932). Moreover,mind-wandering is known to correlate with neural activity in a networkof brain areas that support self-referential processing, known as theDefault Mode Network (DMN) (Mason et al., 2007, Science 315(5810):393;Raichle et al., 2001, Proc. Natl. Acad. Sci. USA 98(2):676; Christoff etal., 2009, Proc. Natl. Acad. Sci. USA 106(21):8719-8724; Simpson et al.,2001, Proc. Natl. Acad. Sci. USA. 98(2):688-693; Buckner R L,Andrews-Hanna J R, & Schacter D L (2008) The brain's default network:Anatomy, function, and relevance to disease, The year in cognitiveneuroscience 2008, eds Kingstone A & Miller M B (Blackwell Publishing,Malden, Mass.), pp 1-38; Andrews-Hanna et al., 2010, Neuron65(4):550-562). This network has been associated with processes rangingfrom attentional lapses to anxiety, as well as to clinical disorderssuch as Attention Deficit Hyperactivity Disorder (ADHD) and Alzheimer'sDisease (Buckner R L, Andrews-Hanna J R, & Schacter D L (2008) Thebrain's default network: Anatomy, function, and relevance to disease,The year in cognitive neuroscience 2008, eds Kingstone A & Miller M B(Blackwell Publishing, Malden, Mass.), pp 1-38; Weissman et al., 2006,Nat. Neurosci. 9(7):971-978; Castellanos et al., 2008, Biol. Psychiatry63(3):332-337).

One potential way to reduce DMN activity is through the practice ofmindfulness-based meditation. Mindfulness, a core element of diverseforms of meditation, is thought to include two complementarycomponents: 1) maintaining attention on the immediate experience, and 2)maintaining an attitude of acceptance toward this experience (Bishop etal., 2004, Clin. Psychol 11(3):230-241). Specific types of mindfulnessmeditation have been taught in a standardized fashion for decades as amainstay of mindfulness training in community and clinical settings(e.g., through traditional teacher- or retreat-led mindfulnessmeditation practice, Mindfulness Based Stress Reduction, MindfulnessBased Cognitive Therapy and Mindfulness Based Relapse Prevention)(Gunaratana H (2002), Mindfulness in Plain English (Wisdom Publications,Somerville, Mass.); Chiesa, 2010, J. Altern. Complement. Med.16(1):37-46; Kabat-Zinn et al., 1985, J Behay. Med. 8(2):163-190;Teasdale et al., 2010, J. Consult. Clin. Psychol. 68(4):615-623; Bowenet al., 2009, Substance Abuse 30(4):295-305).

Three standard and commonly used meditation practices are:Concentration, Loving-kindness, and Choiceless Awareness. Throughfocused attention on a single object of awareness (typically thebreath), Concentration meditation is intended to help individualsretrain their minds from habitually engaging in self-relatedpre-occupations (such as thinking about the past or future, or reactingto stressful stimuli) to more present moment awareness (Gunaratana H(2002), Mindfulness in Plain English (Wisdom Publications, Somerville,Mass.)). Loving-kindness meditation is hypothesized to fosteracceptance, both of oneself and others, as well as to increaseconcentration. It is practiced through directed well-wishing, typicallyby repetition of phrases such as “may [I/someone else] be happy”(Gunaratana H (2002), Mindfulness in Plain English (Wisdom Publications,Somerville, Mass.)). Choiceless Awareness is hypothesized to broaden thescope of mindfulness to all aspects of experience, whether during formalmeditation practice or everyday life, via directly attending to whateverarises in one's conscious field of awareness at any moment (Gunaratana H(2002), Mindfulness in Plain English (Wisdom Publications, Somerville,Mass.); Lutz et al., 2008, Trends in Cognitive Sciences 12(4):163-169).During such training, meditators learn to clearly identify whenself-related thoughts, emotions and body sensations are occurring, andto differentiate identification of these from identifying with them(e.g. awareness that anger is present vs. “I am angry”). That is,meditators practice noticing when they are identifying with an object,and when this occurs, to “let go” and bring their attention back to thepresent moment. Across these practices, one common aim is to reverse thehabit of mind-wandering, which has been defined as “thinking aboutsomething other than what [one is] currently doing” (Killingsworth &Gilbert, 2010, Science 330(6006):932). In other words, the meditator'stask is to remain aware from moment to moment, and self-identificationis included in the off-task category of mind-wandering. Importantly,this information processing task, common to all three of thesemeditation techniques, is a training of attention away fromself-reference and mind-wandering—and potentially away from default-modeprocessing.

Clinically, mindfulness training has shown benefit for the treatment ofpain (Kabat-Zinn et al., 1985, J Behav. Med. 8(2):163-190), substanceuse disorders (Bowen et al., 2009, Substance Abuse 30(4):295-305; Breweret al., 2009, Substance Abuse 30(4):306-317), anxiety disorders (Goldinet al., 2009, J. Cognitive Psychotherapy 23(3):242-257), and depression(Teasdale et al., 2010, J. Consult. Clin. Psychol. 68(4):615-623), whilealso helping to increase psychological well-being in non-clinicalpopulations (Kingston et al., 2007, J Psychosom. Res. 62(3):297-300).These outcomes have been associated with changes in basic psychologicalprocesses such as improved attentional focus (Jha et al., 2007, CognAffect Behav Neurosci 7(2):109-119; Lutz et al., 2009, J Neurosci29(42):13418-13427), improved cognitive flexibility (Moore & Malinowski,2009, Conscious Cogn 18(1):176-186), reduced affective reactivity (Farbet al., 2010, Emotion 10(1):25-33; Goldin & Gross, 2010, Emotion10(1):83-91), and modification or shifts away from distorted orexaggerated view of oneself (Goldin et al., 2009, J. CognitivePsychotherapy 23(3):242-257; Farb et al., 2007, Soc Cogn Affect Neurosci2(4):313-322). However, direct links between the meditative practicesthat are part of mindfulness training and changes in neurobiology remainelusive. Investigation of the brain activation patterns during specificmeditation practices may help to identify potential neural mechanisms ofmindfulness training.

Previous studies have examined individuals using meditation techniquesfrom different traditions (e.g. Tibetan Buddhism, Zen Buddhism,Vipassana, Mindfulness-Based Stress Reduction, etc.), and employed awide variety of experimental methods ranging from performance ofdifferent types of meditation, to introduction of emotionally-chargedsounds during meditation, to assessment of functional connectivity (Lutzet al., 2008, Trends in Cognitive Sciences 12(4):163-169; Manna et al.,2010, Brain Res Bull 82(1-2):46-56; Ives-Deliperi et al., 2011, SocNeurosci. 6(3):231-42; Brefczynski-Lewis et al., 2007, Proc Natl AcadSci USA 104(27):11483-11488; Holzel et al., 2007, Neurosci. Lett.421(1):16-21). However, given the methodological differences and in somecases, difficulty in finding appropriately-matched controls, noconsensus has emerged as to what the neural mechanisms of meditationare, or how they may underlie the behavioral changes that have beenobserved after mindfulness training.

Finding the connection between the mind and the brain has fascinatedneuroscientists for centuries. A rich and complex history has emergedaround the study of first-person subjective reporting in pursuit ofunderstanding human experience. Recent technological advances haveincreasingly refined the objective measurement of neuronal processesthat are present during human experience. However, links between thesethird-person measurements and first-person subjective reports have notbeen established due, at least in part, to several methodologicalchallenges that are inherent in these methodologies. One of the mainchallenges in gathering subjective data is that self-reports can beinaccurate or biased (Nisbett & Wilson, 1977, Psychological Review84(3):231). Another difficulty is in capturing and characterizingsubjective experiences that may regularly reside outside of theconscious attention, such as the visual experience of seeing a color, orthe auditory experience of listening to music. Additionally, people varygreatly in their ability to observe and report upon their experiencesdue to variability in the degree of their awareness of the contents oftheir own thoughts (Christoff, et al., 2009, Proc Natl Acad Sci USA106(21):8719-8724) as well as fluctuations in their attention levels(Tononi & Koch, 2008, Ann N Y Acad Sci 1124(1):239-261). Furthermore,the act of generating a contemporaneous introspective report about anexperience may serve to modify the experience itself (Lutz & Thompson,2003, J. Consciousness Studies, 10(9-10): 31-52). For example, duringintrospective states such as meditation, the act of self-reflectionpulls the individual out of the meditative state.

Relating subjective experiences and observed measurements or data seemssimple enough in theory—gather a subjective first-person report about anexperience as contemporaneously with the experience as possible, andgather third-person, objective data about behavior and brain processessimultaneously with the experience, then formulate concepts ofprinciples of mechanisms that might underlie the experience based uponpossible correlations between the two (Chalmers D J (2000), What is aneural correlate of consciousness? In: Neural Correlates ofConsciousness: Empirical and Conceptual Questions, Metzinger T, ed., pp.17-40. MIT Press: Cambridge, Mass.). This study design has proven to bemore difficult in practice than it is in theory, as noted above. Anumber of recent studies have devised strategies for solving thisproblem. For example, in their investigation of the neural basis ofmind-wandering, Christoff and colleagues used experience sampling duringfMRI scanning, where, during a sustained attention task, subjects wereintermittently asked to report where their attention was focused andwhether they were on- or off-task preceding the query (Christoff, etal., 2009, Proc Natl Acad Sci USA 106(21):8719-8724). In this study,when subjects reported being off-task, they showed activation in thebrain's default mode network (DMN), a network that reliably demonstratesinvolvement in both mind-wandering, and self-referential processing(Kelley, et al., 2002, J Cogn Neurosci 14(5):785-794; Northoff, et al.,2006, Neurolmage 31(1):440-457; Weissman, et al., 2006, Nat Neurosci9(7):971-978; Mason, et al., 2007, Science 315(5810):393). Importantly,DMN activation was strongest when subjects were unaware of their ownmind-wandering. Similarly, Hasenkamp and colleagues designed a method inwhich subjective information was simultaneously collected alongside fMRIdata (Hasenkamp, et al., 2012, Neuroimage 59(1):750-60). They instructedmeditators to meditate in the fMRI scanner and to press a buttonwhenever they realized their minds had wandered. They differentiallyanalyzed periods before and after the button press to determine brainactivation patterns that were activated during presumptively differentcognitive states. They, too, found that DMN activation correlated withmind-wandering, and that salience/attention network regions (e.g.,dorsal anterior cingulate) were activated during awareness ofmind-wandering. The objectivity of these studies is much improved overthe use of retrospective recall alone. Nevertheless, a query or buttonpress pulls individuals out of their current mind-state and the amountof subjective information that can be gathered during these types ofexperiments is somewhat limited, potentially leading to reverseinference of the cognitive processes that may actually be active at thetimes of the probes (Poldrack, 2006, Trends in Cognitive Sciences10(2):59-63; Christoff, et al., 2009, Proc Natl Acad Sci USA106(21):8719-8724). Thus, methods are still needed to refine theimproved resolution afforded by using fMRI and self-report together, andto include more detailed subjective accounts in a less disruptivemanner.

Another recent advance in the field of neuroimaging has been thedevelopment of real-time functional magnetic resonance imaging (rt-fMRI)neurofeedback. This technique retains the advantage of collectingobjective sampling data contemporaneously with the event, as in thestudies highlighted above, but has the theoretical advantage of reducedinterference with the ongoing task or mind-state. Rt-fMRI neurofeedbackhas demonstrated preliminary success in several areas, includingtraining the brain to manipulate external computerized devices such asprostheses (deCharms, et al., 2004, Neuroimage 21(1):436-443; Birbaumer,et al., 2008, Current Opinion in Neurology 21(6):634-638), communicatingwith locked-in patients or those previously thought to be in vegetativestates (Owen and Coleman, 2008, Nat Rev Neurosci 9(3):235-243; Monti, etal., 2010, N Engl J Med 362(7):579-589), lie detection (Spence, et al.,2004, Philos Trans R Soc Lond B Biol Sci 359(1451):1755-62; Langleben,et al., 2005, Human Brain Mapping 26(4):262-272), controlling symptomsof chronic pain (deCharms, et al., 2005, Proc Natl Acad Sci USA102(51):18626-18631) and modulating brain activation in regionsassociated with particular cognitive states (Caria, et al., 2007,Neuroimage 35(3): 1238-1246; Caria et al, 2010, Biological Psychiatry68(5):425-432; Hamilton et al., 2011, Human Brain Mapping 32(1):22-31).However, it has not been used as a tool for exploring the correlatesbetween 1st person subjective experience and brain activity. Such amodality would be especially useful in investigating neural correlatesof introspective states that are conceptually difficult to convey in thefirst-person. For example, during the practice of mindfulnessmeditation, individuals practice dropping into states of “bareawareness,” that, by definition, are free of all concepts including theconcept of ‘someone’ paying attention (Gunaratana, H. (2002),Mindfulness in Plain English. Somerville, Mass., Wisdom Publications;Lutz, et al., 2008, Trends in Cognitive Sciences 12(4):163-169).Meditators can report being mindfully aware just after, but not during,moments of mindfulness because the act of observing disrupts the stateitself. In extreme examples, such as absorptive concentration meditativestates, awareness is described as “one-pointed” in the sense thatconscious experience is so focused that it becomes literally a singlepoint of focus (Buddhaghosa, A. (1991), The path of purification:Visuddhimagga, Buddhist Publication Society).

Therefore, there is a need in the art for rt-fMRI neurofeedback systemsand methods to bridge the gap between subjective self-report, asindividuals link their experience (including quality, such as depth)with neural activation in a time-precise manner, but with minimalinterruption of their state from an external probe or otherinterference. The present invention satisfies this need.

SUMMARY OF THE INVENTION

The present invention relates to methods for enhancing a meditativestate of a subject or treating a disease or disorder of a subject bymeasuring the subject's brain activity, presenting a representation ofthe measured brain activity to the subject, and instructing the subjectto reduce the represented brain activity by altering their meditativestate. The methods of the present invention can be used to reducemind-wandering and stress. The methods of the present invention can alsobe used to treat Alzheimer's disease, Attention Deficit HyperactivityDisorder (ADHD), depression, substance use disorders, disorders relatedto stress, and disorders related to mind-wandering.

In one embodiment, the method of the present invention is a method ofenhancing a meditative state of a subject, comprising measuring asubject's brain activity; presenting a representation of the subject'sbrain activity to the subject simultaneously with the measuring of thesubject's brain activity; and instructing the subject to alter theirmeditative state, such that the alteration to their meditative statedecreases PCC activity. In another embodiment, the method of the presentinvention is a method of treating a disease or disorder of a subject,comprising measuring a subject's brain activity; presenting arepresentation of the subject's brain activity to the subjectsimultaneously with the measuring of the subject's brain activity;instructing the subject to enter into a meditative state; andinstructing the subject to reduce the represented brain activity byenhancing their present meditative state.

The present invention also relates to methods correlating a subject'ssubjective report, or other information about the subject, to at leastone neuronal process of the subject. In one embodiment, the method ofthe present invention is a method of correlating a subject's subjectivereport to at least one neuronal process of the subject, comprisingmeasuring a subject's brain activity; presenting a representation of thesubject's brain activity to the subject simultaneously with themeasuring of the subject's brain activity; and instructing the subjectto provide a report of their mental state while viewing the presentedrepresentation of the subject's brain activity. In another embodiment,the method of the present invention is a method of correlatinginformation of a subject with a neuronal process of the subject,comprising detecting levels of brain activity of a subject from at leastone brain region of the subject, and correlating the detected level ofbrain activity with information about the subject, such as the subject'sphysiological stress level, the subject's degree of self-referentialactivation, the subject's depth of meditation, and the subject's depthof “flow” state.

The measured or detected brain activity associated with the methods ofthe present invention can comprise activity in human brain regions suchas the posterior cingulate cortex, dorsal anterior cingulate,dorsolateral prefrontal cortex, posterior parietal cortex, posteriorinsula, and thalamus. The measurement of the subject's brain activity ofthe present invention can be performed using functional MagneticResonance Imaging (fMRI) or electroencephalography (EEG). Therepresentation of the measured brain activity of the present inventioncan be presented via a visual display, an interactive visual display, anauditory signal, or a tactile signal.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, there are depicted in thedrawings certain embodiments of the invention. However, the invention isnot limited to the precise arrangements and instrumentalities of theembodiments depicted in the drawings.

FIG. 1: Experienced meditators demonstrate decreased DMN activationduring meditation. Brain activation in meditators>controls is shown,collapsed across all meditations (relative to baseline): (a) and (b)show activations in the left mPFC and PCC; (c) and (d) show averagepercent signal change (±SD) during individual meditation conditions inthe mPFC and PCC respectively: Choiceless Awareness (green bars),Loving-kindness (red), and Concentration (blue) meditations. Note thatdecreased activation in PCC in meditators is common across differentmeditation types. N=12/group.

FIG. 2: Experienced meditators demonstrate co-activation of PCC, dACC,and dlPFC at baseline and during meditation. Functional connectivitywith the PCC seed region collapsed across all meditation conditions, isshown in (a, i) controls at baseline; (b, j) meditators at baseline (c,k) meditators>controls at baseline; (e, m) controls during meditation;(f, n) meditators during meditation; (g, o) meditators>controls duringmeditation. Connectivity z-scores (±SD) are shown in (d) for dACCcluster from panel c; (h) for dACC cluster from panel g; (l) for leftdlPFC cluster from panel k; and (p) for right dlPFC cluster from panelk. Baseline (white bars), Choiceless Awareness (green bars),Loving-kindness (red bars), and Concentration (blue bars) meditationconditions are shown separately for meditators (left) and controls(right). N=12/group. FWE corrected, p<0.05.

FIG. 3: Experienced meditators demonstrate co-activation of mPFC, insulaand temporal lobes during meditation. Differential functionalconnectivity with mPFC seed region and left posterior insula is shown inmeditators>controls: (a) at baseline and (b) during meditation.Connectivity z-scores (±SD) are shown for left posterior insula in (c).Choiceless Awareness (green bars), Loving-kindness (red), andConcentration (blue) meditation conditions are shown separately. Foreach color, baseline condition is displayed on the left and themeditation period on right. N=12/group. FWE corrected, p<0.05.

FIG. 4: Schematic of rt-fMRI neurofeedback. a) Individuals performed a0.5 min baseline task in which they viewed adjectives and decided ifthese described them, and then were asked to meditate for 3 minutes withtheir eyes open while their brain activation in the posterior cingulatecortex (PCC), or posterior parietal cortex control region, was displayedin the background. During meditation, they were instructed to check thegraph periodically to determine how well it matched their subjectiveexperience. b) Regions of interest: PCC (red, MNI coordinates: −6, −60,18), posterior parietal cortex (yellow, MNI coordinates: −55, −51, 19),and remaining grey matter (light blue).

FIG. 5: Examples of rt-fMRI neurofeedback from meditators. Percentsignal change (corrected for whole brain signal drift) from the PCCduring feedback runs 1-4 are shown for a) subject #80, b) subject #93and c) subject #43.

FIG. 6: Examples of rt-fMRI neurofeedback from controls. Percent signalchange (corrected for whole brain signal drift) from the PCC duringfeedback runs 1-4 are shown for a) subject #70, b) subject #94 and c)subject #95.

FIG. 7: Examples of rt-fMRI neurofeedback from meditator #37. Percentsignal change (corrected for whole brain signal drift) from the PCCduring feedback runs 1-6 are shown (run #5 shows feedback from posteriorparietal cortex).

FIG. 8: Experienced meditators demonstrate relative PCC deactivationduring meditation. Average percent signal change (corrected for wholebrain signal drift) from the PCC during feedback run 7 is shown formeditators (blue) and controls (red). p=0.013.

DETAILED DESCRIPTION

It is to be understood that the figures and descriptions of the presentinvention have been simplified to illustrate elements that are relevantfor a clear understanding of the present invention, while eliminating,for the purpose of clarity, many other elements found in neurofeedbackmethods and systems. Those of ordinary skill in the art may recognizethat other elements and/or steps are desirable and/or required inimplementing the present invention. However, because such elements andsteps are well known in the art, and because they do not facilitate abetter understanding of the present invention, a discussion of suchelements and steps is not provided herein. The disclosure herein isdirected to all such variations and modifications to such elements andmethods known to those skilled in the art.

Definitions

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the present invention, the preferred methodsand materials are described.

As used herein, each of the following terms has the meaning associatedwith it in this section.

The articles “a” and “an” are used herein to refer to one or to morethan one (i.e., to at least one) of the grammatical object of thearticle. By way of example, “an element” means one element or more thanone element.

“About” as used herein when referring to a measurable value such as anamount, a temporal duration, and the like, is meant to encompassvariations of ±20% or ±10%, more preferably ±5%, even more preferably±1%, and still more preferably ±0.1% from the specified value, as suchvariations are appropriate to perform the disclosed methods.

A “disease” is a state of health of an animal wherein the animal cannotmaintain homeostasis, and wherein if the disease is not ameliorated thenthe animal's health continues to deteriorate.

In contrast, a “disorder” in an animal is a state of health in which theanimal is able to maintain homeostasis, but in which the animal's stateof health is less favorable than it would be in the absence of thedisorder. Left untreated, a disorder does not necessarily cause afurther decrease in the animal's state of health.

A disease or disorder is “alleviated” if the severity of a symptom ofthe disease or disorder, the frequency with which such a symptom isexperienced by a patient, or both, is reduced.

The terms “patient,” “subject,” “individual,” and the like are usedinterchangeably herein, and refer to any animal amenable to the methodsdescribed herein. In certain non-limiting embodiments, the patient,subject or individual is a human.

As used herein, “treating a disease or disorder” means reducing thefrequency with which a symptom of the disease or disorder is experiencedby a patient. Disease and disorder are used interchangeably herein.

The term “visual display” refers to any type of device suitable for thepresentation of images. Examples of visual displays include, but are notlimited to, light-emitting diode displays (LED), liquid crystal displays(LCD), cathode ray tube displays (CRT), electroluminescent displays(ELD), plasma display panels (PDP), thin-film transistor displays (TFT),electronic paper, holographic projections, and the like.

The term “interactive visual display” refers to any type of devicesuitable for the presentation of images and also suitable for receivinginput. Examples of interactive visual displays include, but are notlimited to, tablet computers, touch-screen displays, video game systems,and the like.

The term “meditation” refers to a variety of practices or techniquesrelated to contemplation, reflection, engaging in mental exercise,training the mind, inducing a mode of consciousness, concentrationfocus, increasing awareness of the present moment, focused thinking,self-regulation of thought, and the like.

The terms “flow state” refers to the mental state of a subject when thesubject is performing at the subject's peak without distraction oranxiety, i.e. when the subject is most focused, absorbed, relaxed,creative, and the like.

The terms “fMRI feedback,” “rt-fMRI feedback,” “fMRI neurofeedback,”“rt-fMRI neurofeedback,” and the like are used interchangeably herein,and refer to the use of a functional Magnetic Resonance Imaging deviceto display or provide a representation of a subject's brain activity tothe subject in a real-time or substantially simultaneous manner.

Throughout this disclosure, various aspects of the invention can bepresented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, 6 and any wholeand partial increments therebetween. This applies regardless of thebreadth of the range.

DESCRIPTION

The system and methods of the present invention are based on the findingthat the main nodes of the default mode network (DMN) (medial prefrontaland posterior cingulate cortices) are relatively deactivated inexperienced meditators, across all meditation types. Further, functionalconnectivity analysis demonstrates that there is a stronger coupling inexperienced meditators between the posterior cingulate, dorsal anteriorcingulate and dorsolateral prefrontal cortices (regions previouslyimplicated in self-monitoring and cognitive control), both at baselineand during meditation. Due to differences found in the default modenetwork that are consistent with decreased mind-wandering, the presentinvention provides new insight into the neural mechanisms of meditation,including meditation training, control and therapeutic uses thereof.

The present invention further includes systems and methods of usingreal-time fMRI neurofeedback to improve the correspondence betweenfirst-person experience and specific brain activation patterns in amanner that minimally affects the experience itself. The presentinvention also provides meditators the ability to enhance their controlover their own brain activity, such as PCC activation. The presentinvention provides a system and method of using fMRI neurofeedback todirectly correlate subjective experience with neural activation.

As contemplated herein, the system and methods of the present inventioncan use any sort of brain activity imaging hardware and software,including functional magnetic resonance imaging, source-localizedelectroencephalography, or by other means understood by those skilled inthe art. Furthermore, neurofeedback data can be integrated and presentedto the measured subject via any sort of visual, audio or other sensorymechanism, as would be understood by those skilled in the art.Non-limiting examples include a visual display, an interactive visualdisplay (e.g., video game), an auditory signal, or a tactile signal.Information can further be streamed to the subject, if desired.

While the disclosure herein focuses on the DMN, and particularly thePCC, the present invention is suitable for use with any area or regionof the brain, including without limitation, the dorsal anteriorcingulate, dorsolateral prefrontal cortex, posterior parietal cortex,posterior insula and thalamus.

As contemplated herein, the present invention includes systems andmethods for alleviating and/or treating a disease or disorder.Non-limiting examples of the types of treatable diseases or disordersmay include ADHD, Alzheimer's disease, stress-related diseases anddisorders, mind-wandering, depression, mood disorders, substance usedisorders, autism, schizophrenia, and the like.

As contemplated herein, the present invention provides a real-timeneurofeedback mechanism to assist a subject to obtain, maintain,optimize and/or enhance a meditative state or “flow” state. Such statesare conducive to the reduction of disorders or diseases, or thetreatment of such symptoms or conditions as described herein. Thepresent invention provides a platform for a subject to obtain, maintain,optimize and/or enhance a meditative state or “flow” state via theinstruction provided by the feedback system in real-time. Thisinstruction provides the subject the opportunity to augment or altertheir meditative state to obtain the desired brain activity presented tothem in real time.

Thus, the present invention includes a method of enhancing a meditativestate of a subject. The method includes the steps of measuring asubject's PCC activity by fMRI, presenting a representation of thesubject's PCC activity to the subject simultaneously with saidmeasuring, and instructing the subject to alter their meditative state,such that the alteration to their meditative state decreases PCCactivity. In one embodiment, the enhanced meditative state reducesmind-wandering. In another embodiment, the enhanced meditative statereduces stress.

The present invention also includes a method of treating a disease ordisorder of a subject. The method includes the steps of measuring asubject's PCC activity by fMRI, presenting a representation of thesubject's PCC activity to the subject simultaneously with saidmeasuring, instructing the subject to enter into a meditative state, andinstructing the subject to reduce the represented PCC activity byoptimizing their present meditative state. In one embodiment, thedisease is Alzheimer's disease. In another embodiment, the disorder isADHD. In one embodiment, the disorder is stress-related. In oneembodiment, the disorder is mind-wandering.

The present invention also includes a method of correlating a subject'ssubjective report to at least one neuronal process of the subject. Themethod includes the steps of measuring a subject's PCC activity by fMRI,presenting a representation of the subject's PCC activity to the subjectsimultaneously with said measuring, and instructing the subject toprovide a report of their mental state when viewing the presentedrepresentation of the subject's PCC activity. In another embodiment, therepresentation presented is via a visual display, an interactive visualdisplay, an auditory signal, or a tactile signal. The present inventionalso includes a method of correlating information of a subject with aneuronal process of the subject, including the steps of detecting levelsof brain activity of a subject from at least one of the followingspecific brain regions in a human subject: posterior cingulate cortex,dorsal anterior cingulate, dorsolateral prefrontal cortex, posteriorparietal cortex, posterior insula, thalamus, and correlating thedetected level of activity with the subject's physiological stresslevel/indicator(s), degree of self-referential activation, depth ofmeditation, depth of “flow” state occurring at substantially the sametime as the detected brain activity. In one embodiment, the detecting isperformed by fMRI or source-localized electroencephalography.

The systems or methods of the present invention can also includesimultaneously measuring the subject's brain activity in more than onebrain region and presenting a representation of each of the subject'smeasured brain activities to the subject simultaneously to saidmeasuring.

EXPERIMENTAL EXAMPLES

The invention is further described in detail by reference to thefollowing experimental examples. These examples are provided forpurposes of illustration only, and are not intended to be limitingunless otherwise specified. Thus, the invention should in no way beconstrued as being limited to the following examples, but rather, shouldbe construed to encompass any and all variations which become evident asa result of the teaching provided herein.

Without further description, it is believed that one of ordinary skillin the art can, using the descriptions and the illustrative examplesherein throughout, practice the present invention. The followingexamples therefore, are not to be construed as limiting in any way theremainder of the disclosure.

Example 1: Meditation Experience is Associated with Differences in DMNActivity and Connectivity

As contemplated herein, it was hypothesized that the DMN would be animportant locus of change following meditation training. Specifically,it is believed that brain activation during mindfulness meditation inexperienced meditators as compared to their matched controls wouldinvolve (1) relatively reduced recruitment of the DMN, and (2)relatively increased connectivity between DMN and brain structures thatare implicated in monitoring for conflict as well as cognitive control,such as the dorsal anterior cingulate (dACC), and dorsolateralprefrontal cortices (dlPFC) respectively. To test this, fMRI was used toassess brain activation during both a resting state and a meditationperiod in experienced mindfulness meditation practitioners and controls.To determine common neural activation patterns across meditations,participants were scanned during periods of Concentration,Loving-kindness, and Choiceless Awareness meditation.

The following methods were used in experimental example 1:

Subjects

Twelve right-handed individuals with >10 years and an average of10565±5148 hours of mindfulness meditation experience, and thirteenhealthy volunteers were recruited to participate. Right-handedmeditation-naïve controls were case-control matched for country oforigin (US), primary language (English), gender, age, race, education,and employment status. One control participant did not follow directionsand was removed before any analyses were performed. With the exceptionof a single mismatch in gender and age respectively, all participantswere well-matched (e.g. within 3 years of age of their match). Allparticipants gave informed consent in accordance with the procedures ofthe Yale University Human Investigation Committee.

Task

Just prior to scanning, all participants were introduced to threestandard mindfulness meditation instructions: 1) Concentration: “pleasepay attention to the physical sensation of the breath wherever you feelit most strongly in the body. Follow the natural and spontaneousmovement of the breath, not trying to change it in any way. Just payattention to it. If you find that your attention has wandered tosomething else, gently but firmly bring it back to the physicalsensation of the breath.”; 2) Loving-kindness: “please think of a timewhen you genuinely wished someone well (pause). Using this feeling as afocus, silently wish all beings well, by repeating a few short phrasesof your choosing over and over. For example: May all beings be happy,may all beings be healthy, may all beings be safe from harm.”; and 3)Choiceless Awareness: “please pay attention to whatever comes into yourawareness, whether it is a thought, emotion, or body sensation. Justfollow it until something else comes into your awareness, not trying tohold onto it or change it in any way. When something else comes intoyour awareness, just pay attention to it until the next thing comesalong.” Participants practiced each meditation type outside of thescanner and confirmed that they understood and could follow theinstructions before proceeding. Each run began with a 2-minuteresting-state baseline period (“please close your eyes and don't thinkof anything in particular”), which is consistent with standardresting-state induction procedures (Raichle et al., 2001, Proc. Natl.Acad. Sci. USA 98(2):676; Castellanos et al., 2008, Biol. Psychiatry63(3):332-337; Fox et al., 2005, Proc Natl Acad Sci USA102(27):9673-9678). This was followed by a 30-second recorded meditationinstruction (as above), and a 4.5-minute meditation period. Everysubject performed each meditation twice. Meditation conditions werepresented in a random order, but the second instance of each meditationwas blocked (i.e. AABBCC). After each run, participants were asked torate how well they were able to follow the instructions and how muchtheir mind wandered during each meditation period on a scale of 0-10.

Statistical Analysis of Self-Report Data

Multivariate analysis was performed of variance (MANOVA) using SPSS 18(SPSS, Inc; Chicago, Ill.). All tests of significance are reported astwo-tailed, and means are reported with ±standard deviation.

Imaging Data Acquisition

Functional and structural data were acquired on a 3T TRIO Siemens MRIscanner (Siemens Healthcare, Erlangen, Germany) located at Yale'sMagnetic Resonance Research Center. A high resolution, 3-D MagnetizationPrepared Rapid Acquisition Gradient Echo (MPRAGE) T1-weighted sequencewas used to acquire anatomical images (TR=2530 ms; echo time (TE)=3.66ms; Flip angle=7 degrees; Field of view=256×256 mm; Matrix=256×256); and176 1 mm slices). Blood Oxygen Level Dependent (BOLD) functional imageswere acquired with a T2*-sensitive echo-planar image (EPI) gradient-echopulse sequence (TR=2000 ms; TE=25 ms; Flip angle: 85 degrees; Field ofview=220×220 mm; Matrix=64×64; and 32 4 mm slices). Each functional runconsisted of 210 volumes, including an initial rest period of 10 seconds(to achieve signal stability) that was removed from the data prior topreprocessing.

Imaging Data Processing

Functional images were subjected to standard preprocessing using SPMS(Wellcome Department of Cognitive Neurology) following prior publishedmethods (e.g. Kober et al., 2010, Proc Natl Acad Sci USA107(33):14811-14816). This included the following steps: slice scan-timecorrection to the middle slice of each volume; a two-pass realignment ofall functional images, first to the first image of the first functionalscan, and then to an interim computed mean image; co-registration of theanatomical image and the average of these realigned functional images;co-registration of all functional images using the parameters obtainedfrom co-registration of the mean image; application of the SPM UnifiedSegmentation process to the anatomical scan, using prior informationfrom the ICBM Tissue Probabilistic Atlas and estimation of non-linearwarping parameters (Ashburner & Friston, 2005, Neuroimage26(3):839-851); warping the functional images to the MontrealNeurological Institute (MNI) template space, followed by smoothing offunctional images using a 6 mm isometric Gaussian kernel.

GLM Data Analysis

First-level robust regression was performed on each participant'spreprocessed images, using the standard general linear model but withiteratively reweighted least squares using the bisquare weightingfunction for robustness (Kober et al., 2010, Proc Natl Acad Sci USA107(33):14811-14816; Wager et al., 2005, Neuroimage 26(1):99-113), asimplemented in MATLAB 7.3 (Mathworks, Natick, Mass.; robust.m), usingscripts created by H. Kober and J. Weber. Motion parameters andhigh-pass filter parameters were added as additional regressors of nointerest. Activity during each meditation epoch was estimated as percentsignal change from resting baseline. Next, a second-level, randomeffects analysis was performed to estimate group activity during eachmeditation epoch, and to compare activity between groups, using NeuroElf(NeuroElf.net). Results are FamilyWise Error corrected for multiplecomparisons at p<0.05 unless otherwise indicated.

Functional Connectivity Analysis: Region-of-Interest Definition

To assess the connectivity of brain regions with the DMN, two regions ofinterest (ROIs) were defined in mPFC and PCC (MNI coordinates −6, 52, −2and −8, −56, 26 respectively), based on DMN coordinates reportedpreviously (e.g. Andrews-Hanna et al., 2010, Neuron 65(4):550-562).Given that these were located very close to the midplane (X=0) right andleft mPFC and PCC were combined respectively by selecting all voxelswithin a sphere of 10 mm radius around coordinates projectedorthogonally onto the midplane (X=0) of the brain.

Definition of Temporal Segments of Interest

To determine differences in network connectivity, three temporal epochsof 50 volumes/100 seconds each, were defined as follows: 1)resting-state baseline (“please close your eyes and don't think ofanything in particular”—the epoch prior to the instruction to meditate;volumes 6 through 55), 2) an initial meditation phase (immediatelyfollowing the instruction; volumes 76-125) and 3) a later meditationphase (at the end of each of the meditation sessions; volumes 158-207).For each of these segments, seed-correlations were then computed.

ROI Time-Course Preparation

For each of the six meditation sessions (3 types with one repetitioneach), the average time course of the regions of interest (ROIs) wasextracted for the three different 50-volume/100-second segments. Toensure that maps representing the covariance (correlation) betweenregions and other brain areas were as unbiased as possible towardsspurious positive correlation, the average time course of allwhite-matter (WM) voxels was also extracted. White matter is typicallyconsidered to not show any BOLD-related changes, so that any signalvariation in these areas is usually attributed to noise components.Therefore, the ROI time courses were orthogonalized against this WM timecourse.

Generation of First-Level Seed-Correlation Maps

To assess connectivity and between-group differences, separate multiplelinear regression models were computed for each of the segment-by-ROIpairs. The models contained the ROI time course as covariate of interestand the respective WM time course as covariate of no interest (toaccount for fluctuations most likely driven by global signal changes).For each of these models a z-map was computed, reflecting the z-score ineach voxel assessing the likelihood of signal changes being correlatedto the seed under the null hypothesis. The two homonymous maps (stemmingfrom the two segments of equal meditation technique, e.g. earlymeditation for the two Loving-kindness runs) were combined usingStouffer's z method. The rationale behind this approach is that underthe null hypothesis (no effect for simple tests and no differentialeffect for task-difference tests) this measure is normally distributedaround 0, a pre-requisite for subsequent second-level analyses.

Second-Level Random-Effects Statistical Analysis

Using these correlation maps (the initial 9 maps per subject, based on 3meditation types and 3 parts of the timecourses—baseline, early, andlate meditation—which were condensed into 6 maps, whereas the early andlate correlation maps were combined using the Stouffer z method), wecomputed between group differences for the three meditation types.

As expected, experienced meditators reported less mind-wandering duringmeditation relative to controls (F_((1,22))=7.93, p=0.010). This wasapparent for Concentration (controls=4.9±2.9, meditators=3.2±1.3),Loving-kindness (controls=5.0±2.8, meditators=3.2±1.3), and ChoicelessAwareness meditation (controls=6.0±3.1, meditators=3.4±1.5). Acrossgroups, there was also an effect of time (F_((1,22))=5.01, p=0.036),such that reported mind-wandering was greater during the second run ofeach meditation condition (Time 1=4.08±1.9, Time 2=4.48±2.16). Bothmeditators and controls reported being able to follow the instructionsto a high degree for the Concentration (controls=7.5±2.3,meditators=8.1±1.2), Loving-kindness (controls=7.5±2.6,meditators=7.8±1.5), and Choiceless Awareness meditation conditions(controls=8.5±2.0, meditators=7.9±1.3).

To test the hypothesis that meditators would show differential changesin brain activation during meditation relative to controls, abetween-groups whole-brain contrast analysis collapsing across the threemeditation conditions was performed. It was found that relatively lessactivation in meditators compared to controls in the PCC, a primary nodeof the DMN, as well as the superior, middle and medial temporal gyri anduncus (FIG. 1). Also found was a similar pattern in the medialprefrontal cortex (mPFC), another primary node of the DMN, though it didnot survive whole brain correction for significance (cluster size k=33,threshold k=43; FIG. 1).

Next, between-group differences in each meditation condition wereexamined. During the Concentration meditation condition, there wasrelatively less activation in meditators in the PCC, and left angulargyms (FIG. 1d, 2a-b ) as compared to controls. During Loving-kindnessmeditation, there was relatively less activation in meditators ascompared to controls in the PCC, inferior parietal lobule, and inferiortemporal gyms extending into the hippocampal formations, amygdala, anduncus (FIG. 1d, 2c-d ). During Choiceless Awareness, there wasrelatively less activation observed in meditators as compared tocontrols in the superior and medial temporal gyms.

To test the hypothesis that meditators co-activate different brainregions compared to controls when nodes of the DMN become activated,functional connectivity analyses were performed during both baseline andmeditation periods, using a priori defined DMN seed regions from themPFC and PCC (MNI coordinates −6, 52, −2 and −8, −56, 26 respectively)(7). Using the PCC as the seed region, across all meditation conditionssignificant differences were found in connectivity patterns with severalregions, notably the dACC, (FIG. 2e-h ). This pattern of differentialbetween-group connectivity held during the resting-state baseline periodas well, suggesting a stable pattern of connectivity regardless of task(resting-state baseline vs. meditation, FIG. 3a-d ). A similarconnectivity pattern was found between the PCC and dlPFC at baseline(FIG. 2i -1) that was not significantly different between groups duringmeditation due to a relatively lower strength of anti-correlations incontrols (FIG. 2m-p ).

Using the mPFC as the seed region, increased connectivity with thefusiform gyms, inferior temporal and parahippocampal gyri, and leftposterior insula (among other regions) in meditators relative tocontrols during meditation was found (FIG. 3a-c ). A subset of thoseregions showed the same relatively increased connectivity in meditatorsduring the baseline period as well.

As predicted, across all mindfulness meditation conditions, the twoprimary nodes of the DMN (the PCC and mPFC) were less active inmeditators than controls. Also observed were meditation-specificregional differences in activation patterns, such as deactivation in theamygdala during Loving-kindness. Finally, using DMN seed regions,distinct functional connectivity patterns were observed in meditatorsthat differed from controls, and which were consistent acrossresting-state baseline and meditation conditions. These results suggestthat the neural mechanisms underlying mindfulness training areassociated with differential activation and connectivity of the DMN. Asmeditators also reported significantly less mind-wandering, which hasbeen previously associated with activity in DMN, these results supportthe hypothesis that alterations in DMN are related to reduction inmind-wandering. Finally, the consistency of connectivity across bothmeditation and baseline periods suggests that meditation practice maytransform the resting-state experience into one that resembles ameditative state, and as such, is a more present-centered default mode.

The meditation sample was restricted to very experienced meditators froma single practice tradition (mindfulness/insight meditation). This wasintended to reduce heterogeneity in meditation practices. Additionalstrengths of the study include the use of three standardized meditationtechniques that are taught within this tradition, and the utilization ofcontrol subjects that were case-matched for a number of demographicparameters. This kind of matching increases the likelihood of yieldingresults that are both valid and generalizable to individuals in thewestern hemisphere. Furthermore, because experienced meditators train tobe mindfully aware all of the time, and thus may be activating similarbrain regions during both resting-state and meditation, GLM analyses maybe limited due to their dependence upon a relative change from baseline.Therefore, functional connectivity was employed as a complementaryanalytic technique within a single data set. This convergent analysisdirectly addresses the limitations of baseline conditions in previousstudies.

From a theoretical perspective, the view of meditation as consisting oftraining away from mind-wandering and self-identification gave rise toseveral predictions that were confirmed by the data. First, given theprimacy of the DMN in self-referential processing and mind-wandering,the primary prediction was that the DMN would be the main “target” ofmeditation practice, and that alterations in classic DMN activity wouldbe found in experienced meditators relative to controls. Indeed, thoughnot consistently, prior work has suggested alterations in DMN followingbrief meditation training and in experienced meditators. For example,consistent with previous reports of PCC activation during selfing′tasks, Pagnoni and colleagues showed relative activation in the PCC inZen meditators+controls when viewing words vs. scrambled non-wordletters while meditating, though no between-group differences werefound. Further, Farb and colleagues reported that individuals who hadreceived eight weeks of MBSR demonstrated relative deactivation of thePCC when performing a task in which they engaged in awareness ofthoughts, feelings and body sensations when reading personality-trainadjectives, compared to determining what the words meant to thempersonally. However, to date no studies have reported alterations in DMNactivation or functional connectivity during meditation itself.Clarifying this prior work, the data presented herein is novel in thatthey provide direct evidence for this prediction, as meditators showedrelatively decreased activation in mPFC and PCC, the two primary nodesof the DMN during meditation. This finding is especially salient asmeditators reported significantly less mind-wandering during meditationperiods relative to controls. Taken together, and inasmuch as activityin DMN regions reflects self-referential processing and mind-wandering,the current data suggest that meditators are engaged in these processesless than their control counterparts.

A second prediction that emerged from the view of mindfulness as a taskof monitoring and letting go of self-referential thought to keeppresent-focused attention, was that experienced meditators would be morelikely to activate “task positive” brain regions such as thoseimplicated in conflict monitoring, working memory and cognitive control.However, as noted above, it was believed that this may be due to thedependence of GLM analysis on activity during baseline. Thebaseline-independent functional connectivity analyses directly addressedthis confound. It was found that relative to controls, meditators showedincreased connectivity between PCC and task-positive regions, duringresting-state baseline and all meditation conditions, including thoseinvolved in conflict monitoring, cognitive control, and working memory(dACC and dlPFC). These findings suggest that meditators may be on-taskregardless of condition, which also provides a possible explanation forthe relative paucity of between-group differences that were observedwith GLM analyses. Importantly, this increased connectivity with thedACC and dlPFC was not seen using the mPFC as the seed region, which isconsistent with the purported role of the mPFC in integratinginformation gathered from the internal and external environment andrelaying it to the PCC, rather than being directly involved inself-related processing. Interestingly, a study using independentcomponent analysis to assess functional connectivity during a “mindfulawareness” scan after an eight-week MBSR course was recently reported(Kilpatrick et al., 2011, Neuroimage 56(1):290-298). Similar to the mPFCseed-region results presented herein, Kilpatrick et al. found increasedconnectivity between the mPFC and primary interoceptive awarenessregions including the posterior insula. However, Kilpatrick et al. didnot find increased connectivity with other DMN regions, such as the PCC.Several possible explanations for this include 1) the use of differentanalytic tools (ICA vs. a PCC seed region for connectivity analysis) 2)the brief duration of meditation training (eight weeks), and 3) thespecific emphasis on mindful awareness of sounds in the taskinstructions, among others.

Though direct links between white matter tract integrity (e.g. diffusiontensor imaging), brain volume, and functional connectivity are justbeginning to be established, several recent studies of meditation usingthese measures may support our findings. For example, Tang andcolleagues showed improved white matter tract integrity in the vACC anddACC after just 11 hours of Integrative Body-Mind Training meditation(Tang et al., 2010, Proc Natl Acad Sci USA 107(35):15649-15652). Also,Luders et al. found increased white matter integrity in dACC, amongothers in experienced meditators compared to controls (Luders et al.,2011, Neuroimage 57(4):1308-1316). Regarding gray matter density, in anexploratory analysis of individuals who had received MBSR, Holzel andcolleagues found increased gray matter concentration in the PCC.(Holzel, 2011, Psychiatry Res 191(1):36-43) Also, Luders et al. foundincreased gray matter concentration in the inferior temporal gyms inexperienced meditators (Luders et al., 2009, Neuroimage 45(3):672-8).Taken together, these studies of neuronal integrity and brainconcentration may corroborate the findings herein, as these regions wereshown to have increased connectivity in the present study.

The findings from this study support the default-mode interferencehypothesis, which states that the DMN can persist or reemerge duringgoal-directed tasks “to such an extent that it competes withtask-specific neural processing and creates the context for periodicattentional intrusions/lapses and cyclical deficits in performance”.This hypothesis has been built from observations of decreased activityin the task-positive network and increased activity in the DMN duringmindlessness. It has been further supported by the demonstration thatstimulant (nicotine) administration enhances attention by deactivatingareas of the DMN such as the PCC. More importantly, pathological stateshave shown altered DMN connectivity and anti-correlations with thetask-positive network. However, no studies have shown convergence of thetwo networks, in states of well-being or otherwise. With fewer reportedattentional lapses, decreased mPFC and PCC activation during meditationand increased connectivity patterns between DMN and self-control regionsof the brain, the data presented herein provides corollary support forthe interference hypothesis. Moreover, functional connectivity dataherein suggest that meditation practice may couple primary nodes ofthese networks in a potentially beneficial way—temporally linking thePCC to monitoring and self-control regions such that when regions of theDMN emerge to “interfere” with a task, control regions may co-activateto monitor and/or dampen this process. This co-activation ofmonitoring/control regions along with nodes of the DMN may, over time,become a new ‘default mode’ that can be observed during resting-state aswell as during meditation.

Finally, the findings from this study have several clinicalimplications, as a number of pathological conditions have been linked todysfunction within areas of the DMN. For example, ADHD is characterizedby attentional lapses. The majority of research on the pathophysiologyof ADHD has centered on frontal-striatal circuitry, but recent studieshave begun to explore other mechanisms including activity in, andconnectivity with nodes of the DMN. In particular, Castellanos andcolleagues found decreases in anti-correlations between the PCC and dACCin individuals with ADHD. Individuals who have undergone mindfulnesstraining, during which they try to minimize attentional lapses, may bean interesting contrast to those with ADHD. Indeed, mindfulness traininghas shown preliminary efficacy in treating this disorder, but how itaffects brain function in individuals with ADHD remains unknown. Thedata presented herein demonstrates that mindfulness may help to enhancePCC-dACC connectivity in individuals with ADHD, which may correlate withreduced attentional lapses. Another pathological condition that has beenlinked to DMN activity is Alzheimer's disease. Sustained neuronalactivity has recently been linked to increased amyloid-[beta]deposition, potentially explaining the connection between prolongedmetabolic activation in the DMN and Alzheimer's, as well as the linksbetween education levels and delay of onset. Results from the presentstudy suggest that meditation is a way to decrease DMN activity in arelatively specific manner, using simple instructions and at low cost.As such, meditation may also bring with it the advantage of beingaccessible to many individuals regardless of educational and economicbackground. Meditation may also be a way to delay the onset ofAlzheimer's disease. Without limitation to particular clinicalimplications, the findings herein demonstrate group differences in theDMN that are consistent with a decrease in mind-wandering in experiencedmeditators, and provide a basis for a new understanding of the neuralbases of mindfulness meditation practice.

Example 2: First-Person Experience of Mind-Wandering Correlate withIncreased PCC Activation, and Meditation Correlates with DecreasedActivation in this Brain Region

In the following experimental example, rt-fMRI neurofeedback is used totest the feasibility of linking subjective self-report of a meditativestate to neural activity in the PCC. The PCC is selected for severalreasons. First, it has been implicated as a central node of the DMN.Second, the PCC is specifically and robustly deactivated duringdifferent types of meditation. In is hypothesized that individualreports of first-person experience of mind-wandering or other types ofself-referential activity would correlate with increased PCC activationand that meditation would correlate with decreased activation in thisbrain region. Further, it was hypothesized that individuals would beable to discriminate between PCC activity and activity in the posteriorparietal cortex, a region in the DMN that is tightly temporally coupledto the PCC but has not been strongly correlated with self-referentialthoughts.

The following methods were used in experimental example 2:

Subjects

22 right-handed experienced meditators and 22 matched novice controlswere recruited to participate. Meditators reported on average 13.9±7.9years and 9249±6799 hours of mindfulness meditation experience (onemeditator practiced both mindfulness and non-dual meditation as primarypractices). Right-handed meditation-naïve controls were case-controlmatched for gender, age, race, education, and employment status (seeTable 1). All participants gave informed consent in accordance with theprocedures of the Yale University Human Investigation Committee.

TABLE 1 Table 1. Baseline characteristics of participants MED CONT TotalF or (n = 22) (n = 22) (n = 44) χ² df p Sex N (%) N (%) N (%) 0 1 1 Male13 (59.1) 13 (59.1) 26 (59.1) Female  9 (40.9)  9 (40.9) 18 (40.9) Age44.7 ± 12.6 42.9 ± 13.8 43.8 ± 13.1 0.185 1 0.67 Race White 22 (100) 22(100) 44 (100) N/A Education level 5.451 3 0.142 Completed graduate/ 14(63.6) 11 (50.0) 25 (56.8) prof training College grad  5 (22.7)  6(27.3) 11 (25.0) Partial college  3 (13.6)  1 (4.5)  4 (9.1) High School 0  4 (18.2)  4 (9.1) Years of Education 17.9 ± 3.1 16.6 ± 3.4 17.3 ±3.3 1.852 1 0.181

Task

Each study run consisted of collecting a 30-second active baseline inwhich individuals viewed adjectives and mentally decided whether or notthe words described them (Kelley, et al., 2002, J Cogn Neurosci14(5):785-794), followed by a 3-minute meditation period in which agraph depicting BOLD percent signal change relative to the averageactivation from the baseline period in either the PCC or the posteriorparietal cortex (control region), was displayed to the subject (see FIG.4a ). An active baseline condition was used to provide a relatively morestandard baseline signal, as it has been argued and recently shown thatexperienced meditators may adopt a more meditative stance during apassive baseline (Holzel, et al., 2007, Neuroscience Letters421(1):16-21; Brewer, et al., 2011, Proc Natl Acad Sci USA108(50):20254-9). The PCC was chosen as an a priori region of interestdue to recent findings showing it's activation during self-referentialprocessing (Kelley, et al., 2002, J Cogn Neurosci 14(5):785-794;Northoff, et al., 2006, Neurolmage 31(1):440-457; Weissman, et al.,2006, Nat Neurosci 9(7):971-978; Mason, et al., 2007, Science315(5810):393), and, importantly, because it is a common area ofdeactivation during different types of meditation. The posteriorparietal cortex was used as a control region as it is a major node ofthe DMN that has been shown not to correlate as strongly withself-referential processing as the PCC (Northoff & Bermpohl, 2004,Trends in Cognitive Sciences 8(3):102; Northoff, et al., 2006,Neurolmage 31(1):440-457; Andrews-Hanna et al., 2010, Neuron65(4):550-562). Thus, in theory, the posterior parietal cortex shouldshow similar patterns of activity to the PCC, but its activity shouldnot correlate as well with subjective reports.

Participants were instructed to meditate with their eyes open, as isstandard in many meditative traditions (instructions below), letting thegraph of BOLD percent signal change stay in the background or off to theside of their awareness, and, from time to time, to check the graph tosee if it correlated with their experience. Participants were instructedthat they would be receiving feedback from a brain region that wasthought to be involved in self-referential processing and that increased(red) signal on the graph reflected self-referential processing(examples of which were mind-wandering, trying to win something orthinking about what they are going to do later), and decreased (blue)signal reflected meditation. They were educated to the fact that, due tothe nature of fMRT signal (i.e. the hemodynamic response function hasbeen shown to have a time lag that peaks between 4-8 seconds afterneuronal activation (Lee, et al., 2005, Appl Psychophysiol Biofeedback30(3):195-204; Bandettini, et al., 1993, Magnetic Resonance in Medicine30(2):161-173), the feedback that they were receiving would show a delayof up to eight seconds from what was happening in their brain. Thus,they were instructed to check the graph particularly after periods ofexcessive mind-wandering or deep meditation, and then immediately returntheir attention to meditation. Their aim was only to determine how welltheir experience correlated with the graph. Participants were alsoinformed that they may receive feedback from different parts of thebrain during different runs, and thus should consider each runindividually. The graph began displaying values directly after theactive baseline period, with an additional value plotted roughly everytwo seconds, concomitant with each new BOLD frame that was collected(TR=2 seconds). Each subject performed six runs total. Runs five and sixwere randomized to show feedback from either the PCC or posteriorparietal cortex. After each run, participants were asked to rate on ascale of 0-10 how well they were able to follow the instructions and howwell their subjective experience of mind-wandering and meditationcorrelated with the graph. Additionally, they were instructed to brieflydescribe how they knew that their experience lined up with meditation.Responses were recorded and transcribed. After the 6^(th) run, subjectswere informed which of the feedback runs (#5 vs. #6) was from thecontrol region (posterior parietal cortex) of the brain.

Meditation Instructions

All participants were instructed in standard mindfulness concentrationmeditation, as follows: “Pay attention to the physical sensation of thebreath wherever you feel it most strongly in the body. Follow thenatural and spontaneous movement of the breath, not trying to change itin any way. Just pay attention to it. If you find that your attentionhas wandered to something else, gently but firmly bring it back to thephysical sensation of the breath” (Gunaratana, H. (2002), Mindfulness inPlain English. Somerville, Mass., Wisdom Publications; Brewer, et al.,2011, Proc Natl Acad Sci USA 108(50):20254-9). Participants practicedmeditation outside of the scanner and confirmed that they understood andcould follow the instructions before proceeding.

Imaging Data Acquisition and Real-Time Registration

Subjects were scanned in a Siemens 1.5 Tesla Sonata scanner. After afirst localizing scan, a high-resolution sagittal scan was collectedusing a magnetization prepared rapid gradient echo (MPRAGE) sequence(TR=2530 ms, TE=3.34 ms, 160 contiguous sagittal slices, slice thickness1.2 mm, matrix size 192×192, flip angle=8°). Next, a T1-weightedanatomical scan (TR=500 ms, TE=11 ms, FoV=220 mm, thickness=4 mm thick,gap=1 mm) was collected with 25 AC-PC aligned axial-oblique slices.After these structural images, acquisition of functional data began inthe same slice locations as the axial-oblique T1-weighted data.Functional images were acquired using a T2* sensitive gradient-recalledsingle shot echo-planar pulse sequence (TR=2000 ms, TE=35 ms, flipangle=90, Bandwidth=1446 hz/pixel, matrix size=64×64, FoV=220 mm,interleaved acquisition). Prior to feedback, a short functional seriesof 10 volumes (first 2 discarded) was collected. This functional serieswas used as the single subject reference space for motion correction andROI analysis (see below). Feedback functional runs consisted of 113volumes with the first two and last volume(s) discarded.

Region of Interest Definition

Gray matter ROIs were defined on a standard template brain (Holmes, etal., 1998, Journal of Computer Assisted Tomography 22(2):324-333) usingBioImage Suite (www.bioimagesuite.org). The first 26 ROIs were definedbased on prior analysis. Three of these ROIs (the left posterior insula,left PCC, and left posterior parietal cortex) were used for pilottesting of rt-fMRI feedback. The left posterior insula was definedanatomically (volume=2869 mm³) using the Yale Brodmann Atlas (Lacadie,C., R. K. Fulbright, J. Arora, R. T. Constable and X. Papademetris(2008), Brodmann Areas defined in MNI space using a new Tracing Tool inBioImage Suite. Human Brain Mapping (abstract)). The PCC and posteriorparietal cortex ROIs were functionally defined. 9 mm diameter spheres(volume=461 mm³) centered at MNI coordinates (−6, −60, 18) for the PCC(local maximum for between-group differences for concentrationmeditation using GLM analysis) and (−55, −51, 19) for the posteriorparietal cortex (significant functional connectivity with PCC inmeditators and controls, but no significant activity from GLM analysis).The final ROI consisted of the remaining gray matter that was notincluded in the previous ROIs (960455 mm³), and was used to control forscanner drift. Feedback displayed to the subjects was only determinedfrom the PCC, posterior parietal, and gray matter drift control ROI.These ROIs are shown in FIG. 4 b.

Real-Time Image Processing

Prior to feedback, the ROIs were transformed from template space tosingle subject reference space through a series of linear and non-linearregistrations. Similar to previous studies, a non-linear transformationwas first applied to warp the template brain to the individual MPRAGE(3D anatomical) image (Martuzzi, et al., 2010, Neuroimage 49(1):823-834;Hampson, et al., 2011, Brain Connectivity 1(1):91-98). Next, theindividual T1 axial-oblique (2D anatomical) image was linearlyregistered to 3D anatomical image. Finally, the short functional serieswas linearly registered to the 2D anatomical image. All transformationswere visually inspected for accuracy and were estimated using theintensity-only component of the method implemented by BioImage Suite aspreviously reported (Papademetris, et al., 2001, Med Image Comput ComputAssist Interv. 3216(2004):763-770).

Real-Time fMRI Neurofeedback

The moment-to-moment feedback signal shown to the subject was determinedthrough a series of preprocessing steps for each frame of the fMRItime-series using the rt-fMRI system described previously (Hampson, etal., 2011, Brain Connectivity 1(1):91-98; Scheinost, D., M. Hampson, J.Bhawnani, M. Qiu, R. T. Constable and X. Papademetris (2011). A GPUAccelerated Motion Correction Algorithm for Real-time fMRI. Human BrainMapping. Quebec City, Canada). Briefly, each slice of an fMRI volume isreconstructed in real-time and each volume is analyzed immediately afteracquisition. This analysis includes motion correction, after which themean activation of in each ROI was calculated for each frame. To accountfor motion correct and partial volume effects near the edge of thebrain, voxels with intensity less than 25% of the overall brain meanwere excluded from the calculations of the mean activation. Second, anyROI measurement with greater than a 10% change from the previous framewas treated as an outlier and was replaced by the previous measurement(0th order interpolation). Next, the ROI measurements were temporallysmoothed based on the last five values with a zero mean, unit varianceGaussian kernel. Percent signal change in the ROI (either the PCC orposterior parietal cortex) as compared to the ROI average value acrossthe 30 second baseline was corrected for scanner drift by subtractingthe percent signal change from the gray matter control region, aspreviously described (deCharms, et al., 2005, Proc Natl Acad Sci USA102(51):18626-18631). This corrected ROI measurement was thengraphically presented to each subject in real-time (e.g., see FIG. 4a )using E-Prime v. 1.2 (Psychology Software Tools, www.pstnet.com). Theentire processing stream from functional volume acquisition to feedbackdisplay required less than one second of delay from data acquisition ofeach new functional brain volume.

Statistical Analysis of Self-Report Data

Statistical analyses were performed using SPSS/PASW 18 (SPSS, Inc;Chicago, Ill.). All tests of significance are two-tailed and means arereported with ±standard deviation.

As expected, all subjects reported success in following the instructions(average scores across all six runs=9.0±0.01). Additionally, individualsreported a high degree of subjective correlation between theirfirst-person experience of self-referential processing correlating withred and meditation correlating with blue (average scores across firstfour runs=7.5±0.24). These were similar among groups(meditators=7.4±0.16, controls=7.7±0.29).

To determine whether individuals were able to discriminate betweenfeedback derived from signals from the PCC (which was hypothesized toshow increased activity during self-referential processing [red] anddecreased activity during meditation [blue]) as compared to the parietalcortex), individuals' subjective reports were compared during runs fiveand six, in which they received feedback from these two regions in arandomized order. As expected, due to the high correlation shownpreviously between the PCC and posterior parietal cortex in both novicesand individuals with meditation experience (Andrews-Hanna, et al., 2010,Neuron 65(4):550-562; Brewer, et al., 2011, Proc Natl Acad Sci USA108(50):20254-9), correlations of subjective self-reports wererelatively high in both regions (PCC=8.1±1.8, posterior parietalcortex=6.9±2.5); however, correlations between subjective experience andfeedback from the PCC were significantly higher than those with theposterior parietal cortex (t₄₂=3.1, p=0.004).

In order to determine the relative sensitivity of rt-fMRI feedback fromthe PCC in correlation with subjective reports, individuals reported ontheir experience directly after each run and at the conclusion of theirentire scanning session. Representative narratives from three meditatorsand three controls are shown below where we present the subject'sreported correlation between self-experience and the displayed feedbackand the subject's brief description of this correlation for feedbackruns 1-4. If subjects were asked any additional questions orclarifications, the question present is preceded by a “Q:” and thesubject's response is preceded by an “A:”. The corresponding feedbackpresented to each subject is shown in FIG. 5 for the meditators and FIG.6 for the controls. Finally, a fourth self-report with additionalresponses for runs 5 and 6 and the post-feedback interview from ameditator is presented in FIG. 7.

SUBJECT A (meditator, FIG. 5a ). Run 1. Reported correlation: 7. How didI know? As I was getting used to looking at the image there was a lot ofself-referential thoughts a lot of worries around it and as I was ableto look away for a period of time and settle back into it. And thelittle red at the end, I don't know, I didn't feel so lost in thought atthat period. Run 2. Reported correlation: 7. The red spots were mechecking in with the graph. Yeah, the first one was definitely mechecking in with the graph. The second one I don't know . . . . But thelittle ones were definitely. Run 3. Reported correlation: 6. The spikeat the beginning was me thinking “I'm doing good look at all the blue Idid” And after that I was starting to feel some discomfort in my feelingand thinking about how to alleviate it. And just getting into thepatience of it and so there was a bit more red than I expected. Run 4.Reported correlation: 7. The smaller spikes again were me thinking aboutthe pain in my finger. And the spikes at the end were me thinking “goodjob there is a lot of blue.”

SUBJECT B (meditator, FIG. 5b ). Run 1. Reported correlation: 8.Generally when . . . there were certain times when I registered thinkingand the thoughts will come and those are the times that corresponded tothe red spikes. Q: There are segments of deeper blue in the middle. Doyou remember anything particular there? A: Yes, there was a slow inhale.I was very focused on the breath and it slowed down. Run 2. Reportedcorrelation: 10. Well it seemed to be just at that same spot where thebreath changes when you inhale and exhale. And it appears to roughlycorrespond with the red spikes. Q: And there were some deep blue in themiddle. Did that correspond to anything particular? A: There are momentswhen the concentration is deep and the breath is very stable. Run 3.Reported correlation: 9. Well there were some thoughts. There were notmany thoughts. There were some subtle thoughts that I thought would showup but didn't show up. Concentration was fairly smooth. Run 4. Reportedcorrelation: 8. Same as before. I had a few more thoughts or concerns inthe beginning, so that seems to be recorded in the more thinking. Q: Andin the end I see some deeper blue. What was going on there? A: There wasa kind of . . . it felt more stable, the breath and the focus.

SUBJECT C (meditator, FIG. 5c ). Run 1. Reported correlation: 8. I wasaware during [the run] that mind was having gaps and differentlyfocusing and strayed from breath. There was a background of thinking.Run 2. Reported correlation: 9. I was aware of being with the object—thebreath. Run 3. Reported correlation: 9. I was conscious of mind beingfocused on breath. A third of the way in I looked at the graph. In themiddle—relaxation, presence. [Then] it veered away. The red at the endwas when I looked at the graph. Run 4. Reported correlation: 10. Therewas a sense of flow, being with the breath—flow deepened in the middle.

SUBJECT D (control, FIG. 6a ). Run 1. Reported correlation: 3. At thebeginning I felt it was correlating well and although I was justbreathing and focusing, I kept seeing red so maybe at the end I gotanxious but before I got anxious it stopped correlating. I was thinkingdid I move or something . . . . I don't think so. Run 2. Reportedcorrelation: 9. At the beginning I wasn't checking in. And then likeevery time when I go and check in with the graph it looks like it isgoing red with a delay but [when I] wasn't paying attention to the graphit looked blue so it looked like it was correlating with the graph. Andtowards the end I was thinking this is really [neat? garbled] and so Iwasn't really paying attention to my breath and so it turned red. Run 3.Reported correlation: 9. At the beginning was left over from the wordsand then every time I check in with the graph, it keeps going red again. . . and when I don't pay attention to the graph and just concentrateon my breathing it drops back into blue and when I didn't check for along time it stayed blue for a long time. And at the end it was me beingreally impressed with this. Run 4. Reported correlation: 6. I feel likeI was focusing on the task, like on my breathing and at some parts I didget anxious. But um yeah I felt like I was focusing on my breathing alot more than before. I kept looking up and it was red all the time.

SUBJECT E (control, FIG. 6b ). Run 1. Reported correlation: 10. I feltlike that I was able to focus on breathing, but occasionally I wouldhave a flickering thought. And I think that what the red shows. Q: I seein the beginning there is a little more sustained blue, did you noticeanything different between that and any of the later parts? A: Ah, Ithink that there was an eyelash that I was bothering my eye a littlebit. And that created a lot of wondering thoughts for a second. Run 2.Reported correlation: 10. I think that I was more wondering thoughts,when I'm not doing it right. It kinda looks like that. Q: And I notice abig red spike in the beginning and a big blue spike in the middle,anything that you noticed that correlated with those? A: I think in thebeginning I wasn't quite sure if I was supposed to start, and I saw thegraph move, and I was like wow, and I tried a lot harder to focus on mybreathing and watch that. Run 3. Reported correlation: 4. I felt likethat I was on, really concentrating on my breathing, but it looks likeon the graph that I had a lot of wondering thoughts. Run 4. Reportedcorrelation: 8. I was able to focus on my breathing, the physicalsensation, and not thinking of breathing. But I felt like that I had[two?] wandering thoughts. Q: I'm sorry, did you say that you weren'tthinking of breathing? A: Yeah, I was focused more on the physicalsensation instead of thinking in and out.

SUBJECT F (control, FIG. 6c ). Run 1. Reported correlation: 5. Because Ifelt like that I was more focused on my breath than the graph says. Run2. Reported correlation: 7. I think I now realize that I thought that Iwas more focused on my breath than I was at the last one, comparing myexperience from the last one to this one. Q: And that blue right there,what did that correlate with? A: Being really focused on my breath,that's the most intensely. Run 3. Reported correlation: 10. It justlooks like that it tracks exactly . . . . I had nothing in, like Iexperienced definite blank spaces, mind wandering. Run 4. Reportedcorrelation: 10. The run looks like what I exactly experienced feeling.Q: And what's the difference between this run and the previous run? A: Ifelt a lot more relaxed this time. Q: Anything else? A: It felt likeless of a struggle to prevent my mind from wandering.

Subject G (meditator, FIG. 7). Run 1. Reported correlation: 8. I don'ttend to have a lot of self reference. I tried to generate some at theend by saying my name over and over. Run 2. Reported correlation: 8. redpeak in the middle—I had a memory of swimming in a pool Run 3. Reportedcorrelation: 7. The second red spike at the beginning [I was] thinkingabout, evaluating the task. A couple of times in the middle [I was]thinking about reporting on what I was noticing Run 4. Reportedcorrelation: 6. Little things. Run 5 (control region). Reportedcorrelation: 6. I don't know what that big red part at the end was. Run6. Reported correlation: 7. [At the] beginning, memory was red, secondred was me discussing it with myself.

This individual also reported the following after finishing the rt-fMRIsession. “Run 1 is interesting because after several minutes of blue, Iwondered if this paradigm actually did measure self-referentialprocessing so I effortfully broke the period of resting in awareness andgenerated a sense of self by saying “[my name], [my name], [my name],”while trying to visualize my face and sense of myself as a subject inthe scanner “doing something”. This produced a large red spike at theend of the run. OK . . . it works . . . interesting. Run 2 also shows anoscillation that I have noticed while meditating. There are certain“memory-images” that recurrently appear in practice, and there have beensets of them over the years. For example, for maybe 2 or 3 years, therewas one memory-image of trees and vines that were on a path near myparents home that would appear recurrently in practice, especially atthe beginning. In run 2, the memory-image is one of swimming at myaunt's swimming pool around age 8. This corresponds with the largest redspike in run 2. In Run 3+4, these two types of oscillations appear inthe larger background of awareness. I also tried to focus more on breathsensations but quickly remembered how strained that felt because itcreated a subject object split of the watcher and breath. Between run 4and run 6 (run 5 was the dummy), the real-time feedback was suggestingsomething that I had not considered: that these memory images, which Iam not in (as an object), show up as red or self-referential. They don'thave the same jarring tension or contracted feel to them as the othertype of oscillation (when subject is separate from the object) so I havejust assumed that nothing much is happening with them and kind ofdisregarded them as relevant. On Run 6, [Reported correlation: 7] I hada familiar memory image appear, one of a pond, willow tree and fields ofmy parents farm. I noticed the strong red deflection in response tothis, although I don't appear in the image. I went back to the image tosee if there was a sense of watcher-subject and noticed that image has asense of being seen through a child's eyes. The somewhat desolatefeeling landscape corresponds to that child's subjectivity. So there isa subject there, even though I never noticed it before, the scannerfeedback made me look for it. If you look at run 6 you can see meexploring the image in a long run of red in the middle. Then Iremembered I wasn't doing the task so I let it go for a while. Then Istarted imagining myself in the future, telling Jud about what I haddiscovered about childhood memories, which you can see clearly in thesecond run of red at the end of run 6. I am sorry that I blew off thedirections, but I learned something new and very subtle about thoserecurring memory-images that I have had for more than a decade.Something I may not have learned otherwise.”

Example 3: Experienced Meditators More Easily Volitionally DecreaseActivation in the PCC

In experimental example 3, it was hypothesized that experiencedmeditators would be able to more easily volitionally decrease activationin the PCC compared to novice controls. Together, these results supportthe feasibility of using rt-fMRI feedback for linking first- andthird-person data, and further establish the sensitivity and specificityof PCC activity for assessing mind-wandering and meditation.

The following methods were used in experimental example 3:

Using rt-fMRI Neurofeedback as Feedback

Recent studies of meditation have suggested that the PCC is a key regionof deactivation during meditation. As meditators and controls appearedto be learning key elements of meditation from their feedback sessionsin Example 2 (relaxed focus, paying attention to the physical sensationsof the breath rather than thinking about them etc.), and to confirmthese literature reports, an additional task was administered to half ofthe subjects who completed Example 2. These individuals were instructedto decrease PCC activation as much as possible. Given meditator'sprevious experience at performing a meditation task in aselfless/non-‘striving’ way and the fact that novices may not be asadept, it was hypothesized that meditators would be able to more easilyintentionally cause a relative decrease in PCC activation compared tocontrols. This is particularly important in that it has beenhypothesized that the key to meditation is “not doing”, suggesting theparadoxical notion that something is happening (attention is being paid,albeit to the sensation of the breath), without someone behind thataction, which would theoretically increase PCC activation (Buddhaghosa,A. (1991), The path of purification: Visuddhimagga, Buddhist PublicationSociety; Goldstein, J. (1993), Insight meditation: the practice offreedom. Boston, Shambhala Publications; Gunaratana, H. (2002).Mindfulness in Plain English. Somerville, Mass., Wisdom Publications).

Subjects were the same as in Example 2 and were recruited sequentiallythroughout the second-half of that study (meditators, n=10; controlsn=11). After completing the 6^(th) run of Example 2, and informingsubjects whether run 5 or 6 was feedback from the control region, theywere given the following instructions: “Now you have gotten a chance tosee how activation of this region (the PCC) correlates with meditation.Given what you have learned from the previous runs, in the next run,please see how much you can actively make it go blue.” Subjects thenperformed a single run and BOLD percent signal change in the PCC wascalculated as described in Example 2.

Statistical Analysis of PCC Activation

Statistical analyses were performed on average BOLD percent signalchange values using independent samples t-tests (SPSS/PASW 18 Inc;Chicago, Ill.). All tests of significance are two-tailed, and means arereported with ±standard deviation.

As hypothesized, meditators demonstrated a significant voluntaryreduction in PCC activation during meditation relative to controls(meditators=−0.30±0.32%; controls=0.07±0.29%; t₁₉=−2.73, p=0.013, FIG.8).

Methods for improving the correlation between first-person reports ofsubjective experience and third-person measurement of neuronalactivation have improved over the past few years, though clear hurdlesremain. Rt-fMRI neurofeedback is a relatively new and promisingtechnology that has demonstrated utility in a number of areas. However,it has not been tested for use in correlating first-person subjectiveexperience with third-person objective observation. As demonstratedherein, using rt-fMRI neurofeedback for linking subjective reports toneuronal processes is feasible using the example of mind-wandering andmeditative experience linked to PCC activation (Example 2). Not only didindividuals report correlation between their mental state and theobjective rt-fMRI data, but they also were able to discriminate betweentwo highly temporally linked brain regions (the PCC and the posteriorparietal cortex) in a significant manner, which provides validation tothe accuracy and truthfulness of their reports (Example 2). Finally,meditators significantly voluntarily decreased activation in the PCCcompared to controls, confirming previous reports of the PCC's role inmeditative states (Example 3).

The data presented herein substantiates the proof-of-principal thatusing rt-fMRI neurofeedback in cognitive neuroscience studies isfeasible. In studies designed to explore the correlation betweenself-reported experience of introspective brain states and theirunderlying neuro-mechanisms, such as presented herein, use of rt-fMRIwith self-reports can achieve clear and reproducible results that areeasily accessible to the subjects. Additionally, it obviates possiblereverse inference of correlation between subject reports and objectivedata, as individuals report their direct experience as it correlateswith neuronal activation without interference from a probe etc. It mayalso be useful in confirming or further teasing apart differentialcognitive processes that may be hypothesized to activate certain brainregions. For example, Mason and colleagues linked mind-wandering todefault mode network activation by training individuals ‘to boredom’ ina working memory task and measured average neuronal activity duringblocks of a learned versus a novel working memory task (Mason, et al.,2007, Science 315(5810):393). As demonstrated herein, subjects reportedtheir direct subjective experience of mind-wandering and its relativecorrelation with PCC activation, adding convergent validity andresolution to this theory that had not been demonstrated previously.However, it should be noted that this methodology, when used solely todocument neural correlates of certain mind states (i.e.,non-self-referential focus) is still limited to some degree by theinterruption of the subject as observer, as highlighted by reports ofthe graph becoming red when individuals turned their attention to it.

Several questions that have been put forward in the literature aboutmeditation can begin to be addressed. It sheds light on the question ofwhether default mode brain regions are involved in self-referentialprocesses (i.e., is meditation truly ‘selfless’?) by showing linksbetween thinking about ‘me’ (self-referential focus) and increasedactivity in the PCC and, conversely, between a meditative(non-self-referential) focus and decreased activation of the PCC.Another question is, how does subjective relaxation affect meditation(and related PCC activity)? Indeed, subjective experiences both innovices and meditators from Example 2 give the first direct clues that arelaxed awareness of phenomena (e.g. the breath) is necessary fordeactivation of regions involved in self-referential processing (e.g.,the PCC), as has been previously hypothesized (Goldstein, J. (1993),Insight meditation: the practice of freedom. Boston, ShambhalaPublications; Gunaratana, H. (2002), Mindfulness in Plain English.Somerville, Mass., Wisdom Publications; Taylor, et al., 2011, Neuroimage57(4): 1524-1533). Example 3 indirectly supports this as well, asnovices demonstrated an increase in PCC activation on average while‘trying’ to make their graph go blue, while experienced meditators weresignificantly more effective at causing their graphs to be blue by‘allowing’ themselves to drop into a meditative state. This may indicatethe critical difference between ‘someone’ making something happen and‘dropping into’ meditation, and/or “may reflect an adaptive processthrough which present-moment awareness is enhanced in individuals withlong-term meditation experience, and information in the environment isprocessed with reduced distractibility and interference fromself-referent thought or ruminative processes” (Taylor, et al., 2011,Neuroimage 57(4): 1524-1533).

As brain activity is notoriously ‘noisy’, rt-fMRI neurofeedback may beable to add a degree of sensitivity in linking subjective experiencewith neuronal activity that may otherwise be lost with block-designaveraged regional activity. For example, Subject C (FIG. 5c ) reported adeepening of the meditative state that correlated with significantlyreduced PCC activation in run 4 vs. run 3. Additionally, Subject Greported nuanced experience that was linked to very minor changes inneuronal activity. Especially in individuals who may be trained toproficiency in particular tasks, such as meditation, these techniquesmay be remarkably suited for probing and refining first-person data(Lutz & Thompson, 2003, Journal of Consciousness Studies, 10(9-10):31-52).

Real-time fMRI neurofeedback has been used in studies in whichindividuals empirically learned to train specific brain regions. Thesestudies use trial-and-error to train subjects to literally gain controlof their brains. This can be very time consuming, results are oftenvariable and some individuals never acquire control (deCharms, 2008, NatRev Neurosci 9(9):720-729). As observed by Hampson, et al., individualstrained in this manner will often continue to try novel methods evenafter a successful method has been found (Hampson, et al., 2011, BrainConnectivity 1(1):91-98). This continued experimentation with differentmethods could confound these data sets. However, in instances in whichcognitive techniques have been honed over many years (or centuries, inthe case of meditation), this technology may be useful for confirming‘correct’ techniques, especially those that are purely mental. Withmeditation, instruction can be maddeningly simple (e.g., “when sitting,just sit”), and thus conceptually difficult to convey. Surprisingly,several instances were found in which individuals spontaneously learnedseemingly important distinctions between self-related processes, asindicated at first by increased PCC activation and later confirmed bytheir own subjective experience. For example, Subject E reported payingattention to the breath in run #3, but then noticed the differencebetween feeling the physical sensation of the breath as compared tothinking about it in the next run, which was highlighted by a completereversal of PCC activation (FIG. 5b ). Also, Subject F noticed thatrelaxation related to decreased PCC activation, while Subject Gdiscovered a very subtle subjectivity related to an affective tone(“desolate feel . . . ”) during meditation that s/he hadn't noticedbefore. These suggest that rt-fMRI neurofeedback may be used to augmenttraditional meditation instruction. As a yoga teacher provides feedbackto guide one's posture, so too might a machine, one day, providefeedback on neuronal activation that correlates with ‘striving’,‘relaxation’ and possibly even ‘selfing.’

The data presented herein demonstrates the feasibility of using rt-fMRIneurofeedback for more tightly linking 1^(st) person subjectiveexperience with 3^(rd) person objective measurement without interruptingthe on-going experience. These results also demonstrate that this typeof feedback can be sensitive and relatively specific, given a particulartask(s) and brain region. Finally, this data also suggest thepossibility of using rt-fMRI neurofeedback for the augmentation and/orcalibration of specific mental training techniques such as meditation.

The disclosures of each and every patent, patent application, andpublication cited herein are hereby incorporated herein by reference intheir entirety.

While this invention has been disclosed with reference to specificembodiments, it is apparent that other embodiments and variations ofthis invention may be devised by others skilled in the art withoutdeparting from the true spirit and scope of the invention. The appendedclaims are intended to be construed to include all such embodiments andequivalent variations.

1. A method of enhancing a meditative state of a subject, comprising:measuring a subject's posterior cingulate cortex (PCC) activity by fMRI;presenting a representation of the subject's PCC activity to the subjectsimultaneously with said measuring; and instructing the subject to altertheir meditative state, such that the alteration to their meditativestate decreases PCC activity.
 2. The method of claim 1, wherein theenhanced meditative state reduces mind-wandering.
 3. The method of claim1, wherein the enhanced meditative state reduces stress.
 4. The methodof claim 1, wherein the representation of the subject's PCC activity ispresented to the subject via a visual display, an interactive visualdisplay, an auditory signal, or a tactile signal.
 5. The method of claim1, further comprising measuring at least one additional brain activityin the subject's brain selected from the group consisting of thefollowing brain regions: dorsal anterior cingulate, dorsolateralprefrontal cortex, posterior parietal cortex, posterior insula, andthalamus; presenting a representation of the subject's at least oneadditional brain activity to the subject simultaneously with saidmeasuring; and instructing the subject to alter their meditative state,such that the alteration to their meditative state decreases thesubject's at least one additional brain activity.
 6. A method oftreating a disease or disorder of a subject, comprising: measuring asubject's brain activity; presenting a representation of the subject'sbrain activity to the subject simultaneously with said measuring;instructing the subject to enter into a meditative state; andinstructing the subject to reduce the represented brain activity byenhancing their present meditative state.
 7. The method of claim 6,wherein the subject's brain activity is measured by fMRI orsource-localized electroencephalograpy.
 8. The method of claim 6,wherein the subject's brain activity is measured by measuring theactivity of at least one brain region selected from the group consistingof the following specific brain regions in a human subject: posteriorcingulate cortex, dorsal anterior cingulate, dorsolateral prefrontalcortex, posterior parietal cortex, posterior insula, and thalamus. 9.The method of claim 6, wherein the disease is Alzheimer's disease. 10.The method of claim 6, wherein the disorder is Attention DeficitHyperactivity Disorder (ADHD).
 11. The method of claim 6, wherein thedisorder is depression.
 12. The method of claim 6, wherein the disorderis a substance use disorder.
 13. The method of claim 6, wherein thedisorder is stress related.
 14. The method of claim 6, wherein thedisorder is mind-wandering.
 15. The method of claim 6, wherein therepresentation of the subject's brain activity is presented to thesubject via a visual display, an interactive visual display, an auditorysignal, or a tactile signal. 16-18. (canceled)
 19. A method ofcorrelating information of a subject with a neuronal process of thesubject, comprising: detecting levels of brain activity of a subjectfrom at least one brain region selected from the group consisting of thefollowing specific brain regions in a human subject: posterior cingulatecortex, dorsal anterior cingulate, dorsolateral prefrontal cortex,posterior parietal cortex, posterior insula, and thalamus; andcorrelating said detected level of brain activity with at least oneinformation of the subject selected from the group consisting of thesubject's physiological stress level, the subject's degree ofself-referential activation, the subject's depth of meditation, and thesubject's depth of “flow” state; wherein the at least one information ofthe subject selected from the group is occurring at substantially thesame time as the detected brain activity.
 20. The method of claim 19wherein the detecting is performed by fMRT or source-localizedelectroencephalography.